分析代码: def read_stop_line(self): features = load_data.load_json_file(self.all_Line_path)["features"] stop_polygon = [] pedestrian_crossing_polygon = [] for feature in features: if feature["properties"]["subtype"] == 101 or \ feature["properties"]["subtype"] == 105 or \ feature["properties"]["subtype"] == 104: my_polygon = MyPolygon(coordinates=feature["geometry"]["coordinates"][0], properties=feature["properties"]) my_polygon.temporary_properties = {} tree_polygon = my_polygon.polygon tree_polygon.id = feature["properties"]["id"] stop_polygon.append(tree_polygon) stop_line = LineString(fit_polygon(tree_polygon)) my_polygon.temporary_properties["line_string"] = stop_line self.stop_polygon[tree_polygon.id] = my_polygon elif feature["properties"]["subtype"] == 102: my_polygon = MyPolygon(coordinates=feature["geometry"]["coordinates"][0], properties=feature["properties"]) my_polygon.temporary_properties = {} tree_polygon = my_polygon.polygon tree_polygon.id = feature["properties"]["id"] pedestrian_crossing_polygon.append(tree_polygon) self.pedestrian_crossing[tree_polygon.id] = my_polygon self.stop_line_tree = STRtree(stop_polygon) self.pedestrian_crossing_tree = STRtree(pedestrian_crossing_polygon)
时间: 2024-04-29 21:20:41 浏览: 11
这段代码的作用是读取停车线的信息,并将其存储在适当的数据结构中。它首先通过调用 load_data.load_json_file() 函数加载 JSON 文件,其中包含了一些地图要素。然后,它会遍历每个要素,判断其 subtype 是否为 101、105 或 104,如果是,则将该要素转换为 MyPolygon 对象,并将其添加到 stop_polygon 列表中。对于每个 MyPolygon 对象,它还会计算其拟合线段,并将其存储在 temporary_properties 属性中。类似地,如果 subtype 为 102,它会将该要素转换为 MyPolygon 对象,并将其添加到 pedestrian_crossing_polygon 列表中。最后,它会使用 STRtree 对象将停车线和人行横道分别存储在 stop_line_tree 和 pedestrian_crossing_tree 中,以便后续的查询操作。这段代码的主要作用是将停车线存储在适当的数据结构中,以便进行后续的查询和分析。
相关问题
class Queue: def __init__(self, limit=10): self.data = [None] * limit self.head = -1 self.tail = -1
def is_empty(self): return self.head == -1
def is_full(self): return (self.tail + 1) % len(self.data) == self.head
def enqueue(self, value): if self.is_full(): raise ValueError("Queue is full") if self.is_empty(): self.head = 0 self.tail = (self.tail + 1) % len(self.data) self.data[self.tail] = value
def dequeue(self): if self.is_empty(): raise ValueError("Queue is empty") value = self.data[self.head] if self.head == self.tail: self.head = -1 self.tail = -1 else: self.head = (self.head + 1) % len(self.data) return value
def peek(self): if self.is_empty(): raise ValueError("Queue is empty") return self.data[self.head]
补全以下LeNet网络模型代码class LeNet(paddle.nn.Layer): def __init__(self):
class LeNet(paddle.nn.Layer):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = paddle.nn.Conv2D(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2)
self.pool1 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
self.conv2 = paddle.nn.Conv2D(in_channels=6, out_channels=16, kernel_size=5, stride=1)
self.pool2 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
self.fc1 = paddle.nn.Linear(in_features=16*5*5, out_features=120)
self.fc2 = paddle.nn.Linear(in_features=120, out_features=84)
self.fc3 = paddle.nn.Linear(in_features=84, out_features=10)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.pool1(x)
x = self.conv2(x)
x = F.relu(x)
x = self.pool2(x)
x = paddle.flatten(x, start_axis=1, stop_axis=-1)
x = self.fc1(x)
x = F.relu(x)
x = self.fc2(x)
x = F.relu(x)
x = self.fc3(x)
return x